Double-pooling residual classification network based on feature reordering attention mechanism
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Bibliographic record
Abstract
目的针对残差图像分类网络中通道信息交互不充分导致通道特征没有得到有效利用，同时残差结构使部分特征信息丢失的问题，本文提出特征重排列注意力机制的双池化残差分类网络（double-pooling residual classification network of feature reordering attention mechanism，FDPRNet）。方法FDPRNet以ResNet-34 （residual network）残差网络为基础，首先将第1层卷积核尺寸从<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mn mathvariant="normal">7</mn><mo>×</mo><mn mathvariant="normal">7</mn></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/12CB428E-9631-40a4-8731-BC7EAD31219D-M001.jpg"><?fx-imagestate width="7.11199999" height="2.28600001"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/12CB428E-9631-40a4-8731-BC7EAD31219D-M001c.jpg"><?fx-imagestate width="7.11199999" height="2.28600001"?></graphic></alternatives></inline-formula>替换为<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mn mathvariant="normal">3</mn><mo>×</mo><mn mathvariant="normal">3</mn></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/12CB428E-9631-40a4-8731-BC7EAD31219D-M002.jpg"><?fx-imagestate width="7.11199999" height="2.28600001"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/12CB428E-9631-40a4-8731-BC7EAD31219D-M002c.jpg"><?fx-imagestate width="7.11199999" height="2.28600001"?></graphic></alternatives></inline-formula>，保留更多的特征信息，增强网络非线性表达能力，同时删除最大池化层，提高局部细节捕捉能力；然后，提出特征重排列注意力机制（feature reordering attention mechanism，FRAM）模块，将特征图通道进行分组，对其进行组间和组内重排序，通过一维卷积提取各通道组合的特征并拼接，得到重排列特征的权重；最后，提出了双池化残差（double-pooling residual，DPR）模块，该模块使用最大池化和平均池化并行操作特征图，并对池化后的特征图进行逐元素相加和卷积映射，以提取关键特征，减小特征图尺寸。结果在CIFAR-100（Canadian Institute for Advanced Research）、CIFAR-10和SVHN（street view house numbers）数据集上与其他11种图像分类网络进行比较，相比于性能第2的模型RTSA Net-101（residual Net-101 with tensor-synthetic attention），准确率分别提高了1.16%、1.01%和0.98%。实验结果表明FDPRNet显著提升了分类准确率。结论本文提出的FDPRNet具有增强图像通道内部信息交流和减少特征丢失的能力，不仅在分类精度上表现出较高水平，而且显著提升了模型的泛化能力。
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it